As computer systems become ubiquitous in both the home and industry, the ability for any one individual to access applications and data has increased dramatically. Although such ease of access has streamlined many tasks such as paying bills, ordering supplies, and searching for information, the risk of providing the wrong data or functionality to the wrong person can be fatal to an organization. Instances of data breaches at many consumer-product companies and the need to comply with certain statutory measures (e.g., Health Insurance Portability and Accountability Act (HIPAA), Child Online Protection Act (COPA), Sarbanes-Oxley (SOX), etc.) have forced many companies to implement much stricter system access policies.
Historically, computer systems have relied on so-called “logical” authentication in which a user is presented a challenge screen and must provide one or more credentials such as a user ID, a password, and a secure token. In contrast, access to physical locations (e.g., server rooms, file rooms, supply rooms, etc.) is typically secured using physical authentication such as a proximity card or “smart card” that, when presented at a card reader, results in access to the room or area. More recently, these two authentication techniques have been incorporated into single-system access authentication platforms. When used in conjunction with other more complex identification modalities such as biometrics, it has become very difficult to gain unauthorized access to secure systems.
Granting initial access is only half the story, however. Once a user has presented the necessary credentials to gain entry to a secure computer system, for example, he may circumvent the strict authentication requirements by allowing other users to “piggy-back” on his credentials. Users departing from an authenticated session may fail to terminate the session, leaving the session vulnerable to unauthorized access. As a result, sensitive data may be exposed to access by unauthorized individuals.
Many currently available commercial solutions for detecting user presence and departure suffer from significant practical limitations. For example, when “timeouts” are used to terminate system access if keyboard or mouse activity is not detected during a pre-set period of time, the operator's physical presence is insufficient to retain access, and erroneous termination may result in cases of extended passive interaction (e.g., when the user reads materials on the screen). Further, such systems cannot discriminate between different users, and a timeout period introduces the potential for unauthorized use during such period. Approaches that use radio-frequency (RF) or similar token objects to detect user departure based on an increase in distance between the token object and a base transceiver suffer from an inability to reliably resolve the distance between the token and receiver, which can result in a restricted or unstable detection zone. Furthermore, the token objects can be readily swapped or shared.
Yet another solution involves detecting and tracking an operator visually. For example, operator detection and/or identification may be achieved using one or more video cameras mounted to the computer terminal in conjunction with object-recognition techniques (e.g., based on analysis of one or a sequence of images) to detect and locate a single operator, which generally involves differentiating the operator from non-operators and the background scene. Once an operator is identified, her movements within a predefined detection zone, such as a pyramidal volume extending radially outward from the secure computer terminal, are tracked to determine when and whether she interacts with the secure system. In certain implementations, this is done without having to continually re-identify the operator, instead relying on following the motion of the operator with the help of computer-vision motion analysis and other techniques. The position and size of the operator may be tracked to detect when she exits the detection zone, which is called a “walk-away event.” The reappearance of the operator after an absence from the detection zone may also be detected. For example, a stored exemplar of previously identified operators may be used to detect and authenticate the operator upon reappearance and within a pre-defined time window.
One problem associated with currently available visual presence-detection systems is their reliance on relative face sizes to identify the operator among multiple people detected in the field of view of the camera. While, on average, the operator's face (due to his proximity to the camera) appears largest in the image, variations in people's head sizes as well as different hair styles and head covers that occlude the face to varying degrees can result in the misidentification of the operator. An even greater problem of conventional systems is the high rate of false alarms signaling walk-away events. This issue arises from the use of color, intensity, and/or gradient information (or similar two-dimensional cues) in the images to compare tracked foreground patches in previous image frames to query patches in the current frame. If background objects have cues similar to those of the tracked foreground object, which is generally true for faces, false matches are frequently generated—e.g., the face of a person in the background may be incorrectly matched to the face of the operator in a previous image. Thus, when the person in the background subsequently leaves the scene, a walk-away event is falsely declared, and, conversely, when the person in the background remains in the scene, the operator's departure goes unnoticed by the system.
A need exists, accordingly, for improved visual approaches to presence detection and, in particular, for systems and techniques that detect walk-away events more reliably.